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CS 559

Deep Learning

Deep learning is fundamentally about learning hierarchical representations directly from data by training large parameterized models end-to-end with gradient descent, and this graduate course treats it as such — starting from loss surfaces and backprop, then building up to the architectures (CNNs, RNNs/attention, VAEs/GANs, deep RL) that dominate modern vision and language work. Expect a written midterm, a homework, a literature survey with a presentation, and a sizable course project where you read recent papers and implement something nontrivial in a framework like PyTorch. It assumes you are already comfortable with linear algebra, probability, and classical ML at the level of CS 464/maybe Murphy or Bishop, and it is the standard launching pad at Bilkent for thesis work in computer vision, NLP, or generative modeling.

Credit3ECTS5FacultyFaculty of EngineeringBölümComputer Engineering

→ STARS müfredatı (resmi syllabus)

Değerlendirme 100% — 4 adım

30%
20%
15%
35%
Midterm:Essay/written Midterm 30%
Homework Homework 20%
Literature Survey & Presentation Literature Survey & Presentation 15%
Project Project 35%

Önerilen kaynaklar 3 kitap

📖
Önerilen
Deep Learning
I. Goodfellow, Y. Bengio
A. Courville · 2016
📖
Önerilen
Machine Learning: A Probabilistic Perspective
K. P. Murphy
2012 · MIT Press
📖
Önerilen
Pattern Recognition and Machine Learning
C. M. Bishop
2006 · Springer

Haftalık müfredat 14 hafta

Hafta 1
Introduction, course structure, overview of machine learning and deep learning. Hallmarks of deep learning. Linear classifiers.
Hafta 2
Optimization. Stochastic gradient descent and contemporary variants, back-propagation.
Hafta 3
Feedforward networks and training. Activation functions, initialization, regularization, batch normalization, model selection, ensembles.
Hafta 4
Feedforward networks and training; Activation functions, initialization, regularization, batch normalization, model selection, ensembles
Hafta 5
Convolutional neural networks. Fundamentals, architectures, pooling, visualization.
Hafta 6
Convolutional neural networks. Fundamentals, architectures, pooling, visualization.
Hafta 7
Deep learning for spatial localization. Transposed convolution, efficient pooling, object detection, semantic segmentation.
Hafta 8
Recurrent neural networks. Long-short term memory (LSTM). Language models, machine translation, image captioning, video processing, visual question answering, video processing, learning from descriptions, attention.
Hafta 9
Recurrent neural networks. Long-short term memory (LSTM). Language models, machine translation, image captioning, video processing, visual question answering, video processing, learning from descriptions, attention.
Hafta 10
Recurrent neural networks. Long-short term memory (LSTM). Language models, machine translation, image captioning, video processing, visual question answering, video processing, learning from descriptions, attention.
Hafta 11
Deep generative models. Auto-encoders, variational auto-encoders, generative adversarial networks, auto-regressive models, generative image models, unsupervised and self-supervised representation learning.
Hafta 12
Deep generative models. Auto-encoders, variational auto-encoders, generative adversarial networks, auto-regressive models, generative image models, unsupervised and self-supervised representation learning.
Hafta 13
Deep reinforcement learning. Policy gradient methods, Q-Learning.
Hafta 14
Deep reinforcement learning. Project presentations.

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CS 559 için defter ekibi henüz not yazmadı.

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Geçmiş GPA dağılımı 9 dönem · ort. 3.49

DönemCourse CPA
2025-2026 Fall 3.59 1 sec · 40 öğr
2024-2025 Fall 3.42 1 sec · 40 öğr
2023-2024 Fall 3.59 1 sec · 41 öğr
2022-2023 Fall 3.16 1 sec · 27 öğr
2021-2022 Fall 3.48 1 sec · 17 öğr
2020-2021 Spring 3.40 1 sec · 39 öğr
2019-2020 Spring 3.55 1 sec · 39 öğr
2018-2019 Spring 3.56 1 sec · 35 öğr
2016-2017 Spring 3.62 1 sec · 27 öğr

Aggregate course GPA — Bilkent STARS'tan public data. Hoca-bazlı per-section detayı için STARS evaluation report →. Öğrenci anket cevapları KVKK kapsamında defter'de tutulmaz.

⚠️ FZ engelleyen şartlar

There is no final exam for this course, however, any one of the following will directly result in an F grade: (1) not submitting a project or homework (including report), (2) not preparing/presenting a survey on the pre-scheduled date, (3) being absent in the midterm, (4) being absent in a project presentation.

Hocalar 0 bu dönem · 2 geçmiş

Geçmişte ders veren (2 kişi)
Hamdi Dibeklioğlu, Ramazan Gökberk Cinbiş